Applying and Combining Three Different Aspect Mining Techniques

Understanding a software system at source-code level requires understanding the different concerns that it addresses, which in turn requires a way to identify these concerns in the source code. Whereas some concerns are explicitly represented by program entities (like classes, methods and variables) and thus are easy to identify, crosscutting concerns are not captured by a single program entity but are scattered over many program entities and are tangled with the other concerns. Because of their crosscutting nature, such crosscutting concerns are difficult to identify, and reduce the understandability of the system as a whole. In this paper, we report on a combined experiment in which we try to identify crosscutting concerns in the JHotDraw framework automatically. We first apply three independently developed aspect mining techniques to JHotDraw and evaluate and compare their results. Based on this analysis, we present three interesting combinations of these three techniques, and show how these combinations provide a more complete coverage of the detected concerns as compared to the original techniques individually. Our results are a first step towards improving the understandability of a system that contains crosscutting concerns, and can be used as a basis for refactoring the identified crosscutting concerns into aspects.Comment: 28 page

An Evaluation of Clone Detection Techniques for Identifying Crosscutting Concerns

Code implementing a crosscutting concern is often spread over many different parts of an application. Identifying such code automatically greatly improves both the maintainability and the evolvability of the application. First of all, it allows a developer to more easily find the places in the code that must be changed when the concern changes, and thus makes such changes less time consuming and less prone to errors. Second, it allows a developer to refactor the code, so that it uses modern and more advanced abstraction mechanisms, thereby restoring its modularity. In this paper, we evaluate the suitability of clone detection as a technique for the identification of crosscutting concerns. To that end, we manually identify four specific concerns in an industrial C application, and analyze to what extent clone detection is capable of finding these concerns. We consider our results as a stepping stone toward an automated "concern miner" based on clone detection

Discovering faults in idiom-based exception handling.

ABSTRACT In this paper, we analyse the exception handling mechanism of a state-of-the-art industrial embedded software system. Like many systems implemented in classic programming languages, our subject system uses the popular return-code idiom for dealing with exceptions. Our goal is to evaluate the fault-proneness of this idiom, and we therefore present a characterisation of the idiom, a fault model accompanied by an analysis tool, and empirical data. Our findings show that the idiom is indeed fault prone, but that a simple solution can lead to significant improvements

A Declarative Evolution Framework for Object-Oriented Design Patterns

Object-oriented design patterns and high-level refactorings are popular means of implementing and evolving large object-oriented software systems. Unfortunately, these techniques are inadequately supported at implementation level by current-day software development environments. To alleviate this problem, we propose to use the promising technique of declarative metaprogramming. It offers a tight, yet flexible, symbiosis between a base language and a metalevel declarative reasoning engine. It provides a uniform and language-independent way to specify design patterns and transformations declaratively, to instantiate patterns and generate code for them, and to deal with the evolution of these pattern instances. Providing support for evolution of a software system in terms of the design pattern instances it uses is the main emphasis of this paper. 1

A Reflective Approach to Dynamic Software Evolution

In this paper, we present a solution that allows systems to remain active while they are evolving. Our approach goes out from the principle of separated concerns and has two steps. In the first step, we have to make sure that the system's evolvable concerns are cleanly separated. We propose aspect mining and static refactorings for separating those concerns. In a second step, we allow every concern to evolve separately. We present a preliminary reflective framework that allows dynamic evolution of separate concerns

Induced Intentional Software Views

Evolving and maintaining software requires adequate documentation of its implementation. However, due to the software's constant evolution, the documentation and implementation do not remain synchronised. Intentional software views have been proposed as a documentation technique to alleviate this problem. Creating such views is not at all a trivial task, however. In this paper, we propose to use a learning algorithm that derives such intentional software views from extensional software views, which are much easier to build. The resulting approach combines the advantages of intentional software views with the ease of constructing extensional views